Imbalanced Data Streams Research Articles

AOCBLS: A novel active and online learning system for ECG arrhythmia classification with less labeled samples

Electrocardiogram (ECG) is a pivotal determinant of cardiac arrhythmia. In practice, ECG data are often acquired as continuous unlabeled chunks with high labeling costs and severe class imbalances. This poses formidable challenges to existing offline learning methods. To address these issues, we propose a novel model called active online class-specific broad learning system (AOCBLS) to learn streaming imbalanced ECG and classify arrhythmias. First, a two-stage active learning strategy combining diversity and uncertainty queries was designed to select the most valuable beats from the current chunk for further labeling and learning, thereby reducing labeling costs. Subsequently, a stable training weight update rule was developed to address the dynamic imbalances in the streaming data. Finally, an incremental learning algorithm was proposed to update network weights online using selected beats, eliminating the need for retraining from scratch. Thus, the AOCBLS can effectively learn online from imbalanced data streams with less label information. Intra-patient experiments on the benchmark MIT-BIH arrhythmia database showed our model achieved an overall accuracy of 99.10% and a G-mean of 96.35% in identifying the four AAMI-recommended beat types, with only 10% additional annotation, outperforming advanced methods at lower labeling cost. Additionally, patient-specific experimental results on the benchmark database and two other databases, INCARTDB and SVDB, suggest the potential of AOCBLS for personalized ECG monitoring on portable devices.

Online imbalance learning with unpredictable feature evolution and label scarcity

Recently, online learning with imbalanced data streams has aroused wide concern, which reflects an uneven distribution of different classes in data streams. Existing approaches have conventionally been conducted on stationary feature space and they assume that we can obtain the entire labels of data streams in the case of supervised learning. However, in many real scenarios, e.g., the environment monitoring task, new features flood in, and old features are partially lost during the changing environment as the different lifespans of different sensors. Besides, each instance needs to be labeled by experts, resulting in expensive costs and scarcity of labels. To address the above problems, this paper proposes a novel Online Imbalance learning with unpredictable Feature evolution and Label scarcity (OIFL) algorithm. First, we utilize margin-based online active learning to selectively label valuable instances. After obtaining the labels, we handle imbalanced class distribution by optimizing F-measure and transforming F-measure optimization into a weighted surrogate loss minimization. When data streams arrive with augmented features, we combine the online passive-aggressive algorithm and structural risk minimization to update the classifier in the divided feature space. When data streams arrive with incomplete features, we leverage variance to identify the most informative features following the empirical risk minimization principle and continue to update the existing classifier as before. Finally, we obtain a sparse but reliable learner by the strategy of projecting truncation. We derive theoretical analyses of OIFL. Also, experiments on the synthetic datasets and real-world data streams to validate the effectiveness of our method.

Bin.INI: An ensemble approach for dynamic data streams

Class imbalance and concept drifts could deteriorate the performance of classifiers in data stream learning as their co-occurrence presents a complicated learning scenario. This situation becomes more complicated if missing values appear in the data. To tackle this challenge, this paper proposes a novel ensemble method, BINary classification in Incomplete, Non-stationary, and Imbalanced data streams (Bin.INI). As part of Bin.INI, Adapt2Regress builds a linear regression models pool using completely available minority subspaces, monitored under a novel sigmoid-decay function to provide imputation support in a drifting data environment. We use Min++ to balance the data in terms of imbalance complexity, a resampling component proposed earlier. Additionally, the 2-STage Ensemble Pruning technique (2STEP) is proposed which groups the classifiers based upon multiple diversity measures at the first step and selects the best subset using a novel custom metric, Sigmoid-Weighted Imbalance Score, at the second step. By using Adapt2Regress, Min++, and 2STEP, the proposed method can efficiently deal with incomplete, imbalanced, and drifting data in data streams. Experiments are conducted on 350 highly imbalanced data streams containing a variety of concept drifts. 300/350 data streams contain different ratios of missing data (5%–30%) in the minority space. Experiments reveal a noticeable decline in the performance of state-of-the-art approaches when faced with 15% or more missing data, while Bin.INI maintains its performance.

An online ensemble classification algorithm for multi-class imbalanced data stream

An online ensemble classification algorithm for multi-class imbalanced data stream

Learning evolving prototypes for imbalanced data stream classification with limited labels

Real-world data streams often exhibit long-tailed distributions with heavy class imbalance, posing great challenges for data stream classification, especially in the case of label scarcity and concept drift. Several active learning methods have been proposed to address this problem by selecting the most valuable instances for labeling. However, existing methods often struggle to dynamically identify the most valuable instances that truly represent the current concept while still requiring a large label budget. In this work, we propose a new algorithm, LEPID, to combine dynamic micro-cluster concept modeling and local entropy modeling to select current important concepts and prototypes. Specifically, we give greater weight to concept drift prototypes and minority prototypes to focus more on those regions that represent current concepts. We use a local entropy strategy based on micro-clusters to select the most valuable instances for labeling and reduce the label budget. Extensive experiments on real-world and synthetic imbalanced datasets show that, compared to state-of-the-art algorithms, our method can naturally adapt to concept drift and dynamically capture the current and most valuable prototypes to achieve better results even in the case of label scarcity.

Imbalance-Robust Multi-Label Self-Adjusting kNN

In the task of multi-label classification in data streams, instances arriving in real time need to be associated with multiple labels simultaneously. Various methods based on the k Nearest Neighbors algorithm have been proposed to address this task. However, these methods face limitations when dealing with imbalanced data streams, a problem that has received limited attention in existing works. To approach this gap, this paper introduces the Imbalance-Robust Multi-Label Self-Adjusting kNN (IRMLSAkNN), designed to tackle multi-label imbalanced data streams. IRMLSAkNN’s strength relies on maintaining relevant instances with imbalance labels by using a discarding mechanism that considers the imbalance ratio per label. On the other hand, it evaluates subwindows with an imbalance-aware measure to discard older instances that are lacking performance. We conducted statistical experiments on 32 benchmark data streams, evaluating IRMLSAkNN against eight multi-label classification algorithms using common accuracy-aware and imbalance-aware measures. The obtained results demonstrate that IRMLSAkNN consistently outperforms these algorithms in terms of predictive capacity and time cost across various levels of imbalance.

A comprehensive ensemble classification techniques detecting and managing concept drift in dynamic imbalanced data streams

A comprehensive ensemble classification techniques detecting and managing concept drift in dynamic imbalanced data streams

Dynamic Classification Ensembles for Handling Imbalanced Multiclass Drifted Data Streams

Machine learning models often encounter significant difficulties when dealing with multiclass imbalanced data streams in nonstationary environments. These challenges can lead to biased and unreliable predictions, which ultimately impact the overall performance of the models. To address these issues, we propose an innovative approach that integrates dynamic ensemble selection, an adaptive technique for managing imbalanced multiclass data streams, with a concept drift detector for recognizing stream changes and the K-nearest neighbor (KNN) algorithm to tackle issues related to class overlap. The primary objective was to improve the classification of imbalanced multiclass drifted data streams. The adaptive oversampling method generates synthetic samples to mitigate the issues associated with imbalanced data streams. This method utilizes KNN to ensure that the generated samples do not overlap. To handle incoming data streams, a drift detector assists in deciding whether to retain the existing classifiers or create a new one. Dynamic Ensemble Selection (DES) was utilized to select the most appropriate classifier for incoming data, aiming to optimize the performance of the classification task. The proposed method offers an effective solution for achieving an accurate and resilient classification in the context of imbalanced multiclass drifted data streams. To evaluate the effectiveness of our proposal, we conducted experiments on a variety of datasets, including benchmark datasets, real application stream datasets, and synthetic data streams. The experimental results demonstrate the superiority of our contribution in addressing the challenges posed by imbalanced multiclass drifted data streams.

Online semi-supervised active learning ensemble classification for evolving imbalanced data streams

Concept drift is a core challenge in classification tasks of data streams. Although many drift adaptation methods have been presented, most of them assume that labels of all data are available, which is impractical in many real-world applications. Additionally, the absence of label makes the imbalance ratio of an imbalanced data stream difficultly being obtained in time, providing the inaccurate guidance for resampling and causing poor generalization. To tackle the joint challenges, an online semi-supervised active learning method is proposed to classifier imbalanced data streams with concept drift. A newly-arrived data is first added to the sliding window, and then assigned a pseudo label in terms of its nearest cluster. Meanwhile, semi-supervised clustering algorithm offers its predicted label. Based on the above two predictive labels, cluster-based query strategy provides the criteria for the evaluation and selection of representative instances. More especially, the uncertainty and importance of instances are defined to synthetically evaluate its representativeness. After obtaining true labels of typical ones, ensemble classifier is updated by all instances in current sliding window. Experimental results on 13 synthetic and real data streams indicate that the proposed method outperforms six comparative methods on both G-mean and Recall under various labeling budgets.

Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

AbstractToday's ever‐increasing generation of streaming data demands novel data mining approaches tailored to mining dynamic data streams. Data streams are non‐static in nature, continuously generated, and endless. They often suffer from class imbalance and undergo temporal drift. To address the classification of consecutive data instances within imbalanced data streams, this research introduces a new ensemble classification algorithm called Rarity Updated Ensemble with Oversampling (RUEO). The RUEO approach is specifically designed to exhibit robustness against class imbalance by incorporating an imbalance‐specific criterion to assess the efficacy of the base classifiers and employing an oversampling technique to reduce the imbalance in the training data. The RUEO algorithm was evaluated on a set of 20 data streams and compared against 14 baseline algorithms. On average, the proposed RUEO algorithm achieves an average‐accuracy of 0.69 on the real‐world data streams, while the chunk‐based algorithms AWE, AUE, and KUE achieve average‐accuracies of 0.48, 0.65, and 0.66, respectively. The statistical analysis, conducted using the Wilcoxon test, reveals a statistically significant improvement in average‐accuracy for the proposed RUEO algorithm when compared to 12 out of the 14 baseline algorithms. The source code and experimental results of this research work will be publicly available at https://github.com/vkiani/RUEO.

An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift.

A challenge to many real-world data streams is imbalance with concept drift, which is one of the most critical tasks in anomaly detection. Learning nonstationary data streams for anomaly detection has been well studied in recent years. However, most of the researches assume that the class of data streams is relatively balanced. Only a few approaches tackle the joint issue of imbalance and concept drift. To overcome this joint issue, we propose an ensemble learning method with generative adversarial network-based sampling and consistency check (EGSCC) in this paper. First, we design a comprehensive anomaly detection framework that includes an oversampling module by generative adversarial network, an ensemble classifier, and a consistency check module. Next, we introduce double encoders into GAN to better capture the distribution characteristics of imbalanced data for oversampling. Then, we apply the stacking ensemble learning to deal with concept drift. Four base classifiers of SVM, KNN, DT and RF are used in the first layer, and LR is used as meta classifier in second layer. Last but not least, we take consistency check of the incremental instance and check set to determine whether it is anormal by statistical learning, instead of threshold-based method. And the validation set is dynamic updated according to the consistency check result. Finally, three artificial data sets obtained from Massive Online Analysis platform and two real data sets are used to verify the performance of the proposed method from four aspects: detection performance, parameter sensitivity, algorithm cost and anti-noise ability. Experimental results show that the proposed method has significant advantages in anomaly detection of imbalanced data streams with concept drift.

Retracted: A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

Retracted: A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

Dynamic Ensemble Selection for Imbalanced Data Streams With Concept Drift.

Ensemble learning, as a popular method to tackle concept drift in data stream, forms a combination of base classifiers according to their global performances. However, concept drift generally occurs in local data space, causing significantly different performances of a base classifier at different locations. Thus, employing global performance as a criterion to select base classifier is inappropriate. Moreover, data stream is often accompanied by class imbalance problem, which affects the classification accuracy of ensemble learning on minority instances. To drawback these problems, a dynamic ensemble selection for imbalanced data streams with concept drift (DES-ICD) is proposed. For data arrived in chunk-by-chunk, a novel synthetic minority oversampling technique with adaptive nearest neighbors (AnnSMOTE) is developed to generate new minority instances that conform to the new concept. Following that, DES-ICD creates a base classifier on newly arrived data chunk balanced by AnnSMOTE and merges it with historical base classifiers to form a candidate classifier pool. For each query instance, the optimal combination is constructed in terms of the performance of candidate classifiers in its neighborhood. Experimental results for nine synthetic and five real-world datasets show that the proposed method outperforms seven comparative methods on classification accuracy and tracks new concepts in an imbalanced data stream more preciously.

Smoclust: synthetic minority oversampling based on stream clustering for evolving data streams

Many real-world data stream applications not only suffer from concept drift but also class imbalance. Yet, very few existing studies investigated this joint challenge. Data difficulty factors, which have been shown to be key challenges in class imbalanced data streams, are not taken into account by existing approaches when learning class imbalanced data streams. In this work, we propose a drift adaptable oversampling strategy to synthesise minority class examples based on stream clustering. The motivation is that stream clustering methods continuously update themselves to reflect the characteristics of the current underlying concept, including data difficulty factors. This nature can potentially be used to compress past information without caching data in the memory explicitly. Based on the compressed information, synthetic examples can be created within the region that recently generated new minority class examples. Experiments with artificial and real-world data streams show that the proposed approach can handle concept drift involving different minority class decomposition better than existing approaches, especially when the data stream is severely class imbalanced and presenting high proportions of safe and borderline minority class examples.

Cost-sensitive continuous ensemble kernel learning for imbalanced data streams with concept drift

In stream learning, data continuously arrives over time, often at a very high rate. For imbalanced data streams with concept drift, it becomes essential to simultaneously address classification accuracy and time efficiency. However, existing algorithms currently either demand excessive computational time or exhibit relatively lower classification performance. To address these issues, in this paper, a cost-sensitive continuous ensemble kernel learning method (CCEKL) is proposed to deal with the imbalanced data streams with concept drift. Firstly, for two-class data streams, a novel misclassification cost is proposed to adapt to the changing imbalance rate. Secondly, based on the modified loss function, a continuous kernel learning method is applied to adapt to the changing data distribution. Furthermore, the two-class method is extended to multi-class classification to deal with multi-class dynamic imbalance problems. Finally, we use an ensemble approach to address the proposed algorithm sensitivity to the initial kernel width. To further improve efficiency, a parallel approach is applied to simultaneously train multi-classifiers with different initial kernel widths. Experimental results on 30 data streams reveal that, for binary classification, CCEKL achieves superior balanced accuracy in a shorter training time. For multi-class classification, CCEKL attains better balanced accuracy without requiring excessive training time, surpassing the most of the state-of-the-art baseline algorithms.

An adaptive imbalance modified online broad learning system-based fault diagnosis for imbalanced chemical process data stream

An adaptive imbalance modified online broad learning system-based fault diagnosis for imbalanced chemical process data stream

Cost-sensitive sparse group online learning for imbalanced data streams

Cost-sensitive sparse group online learning for imbalanced data streams

Autonomic active learning strategy using cluster-based ensemble classifier for concept drifts in imbalanced data stream

Systems are becoming autonomous nowadays. In real-world scenarios, the involvement of experts in generating a new decision has to be paid a certain amount of cost. Thus, in these conditions, creating a system with the least expert involvement and costs is the main destination. As a solution, an autonomic active learning strategy with a cluster-based ensemble classifier for concept drifts in imbalanced data stream (AACE-DI) is proposed in this paper. Initially, a cluster-based initializing formula is considered to select the most informative instances for labeling a dynamically changing ensemble classifier. Next, the automatically adjusting uncertainty strategy is introduced to prioritize the most uncertain, representative, and minority class data from imbalanced data stream, which also ensures the least labeling cost. This automatic formula not only outperforms the system but also makes it autonomic. Experimental evaluations are organized using 6 real-world data streams and 16 synthetic data streams with different types of concept drift and imbalanced ratios. To measure performance, evaluators, such as the prequential area under curve, recall, and geometric mean, are considered. Moreover, the proposed AACE-DI method achieves expected outcomes with the least cost as compared with other state-of-the-art learning methods.

ML Pro: digital assistance system for interactive machine learning in production

The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by subject matter experts with knowledge of statistical mathematics, coding, and engineering process knowledge. By having humans and computers work together in a digital assistance system, the special characteristics of human and artificial intelligence can be used synergistically. This paper presents the development of a digital human-centered assistance system for employees in the production and development departments of industrial manufacturing companies. This assistance system enables users to apply production-specific data mining and machine learning techniques without programming to typical tabular production data, which is often inherently high-dimensional, nonstationary, and highly imbalanced data streams. Through tight interactive process guidance that considers the dependencies between machine learning process modules, users are empowered to build and optimize predictive models. Compared to existing commercial and academic tools with similar objectives, the digital assistance system offers the added value that both classical shallow and deep learning as well as generative and oversampling methods can be interactively applied to all feature table use cases for different user modes without programming.

Online Learning From Incomplete and Imbalanced Data Streams

Learning with streaming data has attracted extensive research interest in recent years. Existing online learning approaches have specific assumptions regarding data streams, such as requiring fixed or varying feature spaces with explicit patterns and balanced class distributions. While the data streams generated in many real scenarios commonly have arbitrarily incomplete feature spaces and dynamic imbalanced class distributions, making existing approaches be unsuitable for real applications. To address this issue, this paper proposes a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> nline <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning from <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> ncomplete and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> mbalanced <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> treams (OLI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS) algorithm. OLI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS has a two-fold main idea: 1) it follows the empirical risk minimization principle to identify the most informative features of incomplete feature spaces, and 2) it develops a dynamic cost strategy to handle imbalanced class distributions in real-time by transforming F-measure optimization into a weighted surrogate loss minimization. To evaluate OLI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS, we compare it with state-of-the-art related algorithms in three kinds of experiments. First, we adopt 14 real datasets to simulate three scenarios of incomplete feature spaces, i.e., trapezoidal, feature evolvable, and capricious data streams. Second, based on a benchmark online analyzer, we generate 13 datasets to simulate incomplete data streams with different imbalance ratios. Third, we analyze concept drift in two simulated scenes, i.e., online learning and data stream mining, and verify the adaption of OLI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS on repeated concept drifts and variable imbalance ratios. The results demonstrate that OLI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS achieves a significantly better performance than its rivals. Besides, a real-world case study on movie review classification is conducted to elaborate on our OLI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> DS algorithm's effectiveness. Code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/youdianlong/OLI2DS</uri> .